Automatic Heartbeat Classification for Arrhythmia Using Deep Learning Based on Electrocardiogram Signals

Arrhythmia detection is important for early identification of irregular heart activities to prevent serious complications like stroke, cardiac arrest and many other cardiac diseases. Arrhythmia can be detected through a Holter monitor, event monitor, blood test, Electrocardiogram (ECG), etc. Here, ECG signals are used for automatic classification of heartbeat, which is required for arrhythmia detection. In the past, various machine learning approaches have been used but nowadays deep learning-based approaches are proposed mostly for getting better classification accuracy. In this chapter, a simplistic but robust customized deep learning model is implemented for automatic detection of arrhythmia. This model is performed on a standard dataset which is “MIT-BIH arrhythmia” dataset where an impressive classification accuracy of 99.74% is achieved.